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基于Wiener过程的发动机多阶段剩余寿命预测

黄亮 刘君强 贡英杰

黄亮, 刘君强, 贡英杰等 . 基于Wiener过程的发动机多阶段剩余寿命预测[J]. 北京航空航天大学学报, 2018, 44(5): 1081-1087. doi: 10.13700/j.bh.1001-5965.2017.0383
引用本文: 黄亮, 刘君强, 贡英杰等 . 基于Wiener过程的发动机多阶段剩余寿命预测[J]. 北京航空航天大学学报, 2018, 44(5): 1081-1087. doi: 10.13700/j.bh.1001-5965.2017.0383
HUANG Liang, LIU Junqiang, GONG Yingjieet al. Multi-phase residual life prediction of engines based on Wiener process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 1081-1087. doi: 10.13700/j.bh.1001-5965.2017.0383(in Chinese)
Citation: HUANG Liang, LIU Junqiang, GONG Yingjieet al. Multi-phase residual life prediction of engines based on Wiener process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 1081-1087. doi: 10.13700/j.bh.1001-5965.2017.0383(in Chinese)

基于Wiener过程的发动机多阶段剩余寿命预测

doi: 10.13700/j.bh.1001-5965.2017.0383
基金项目: 

民航联合研究基金 U1533128

详细信息
    作者简介:

    黄亮  男, 硕士研究生。主要研究方向:发动机健康管理

    刘君强  男, 博士, 副教授。主要研究方向:民航信息管理、健康管理

    通讯作者:

    刘君强, E-mail: liujunqiang@nuaa.edu.cn

  • 中图分类号: V263

Multi-phase residual life prediction of engines based on Wiener process

Funds: 

Joint Research Foundation for Civil Aviation U1533128

More Information
  • 摘要:

    针对现阶段发动机的寿命预测研究没有考虑到非线性与多阶段的问题,提出了基于多阶段非线性Wiener过程的航空发动机实时剩余寿命预测的方法。该方法融合了同类型发动机的历史性能退化监测数据与个体发动机的实时监测数据。首先,考虑了发动机性能退化非线性的特点,并采用多阶段Wiener过程建立发动机的性能退化模型。然后,根据发动机的历史性能监测数据,利用极大似然估计和一维搜索方法进行参数先验分布的估计。再次,在先验分布和个体发动机的退化数据的基础上,用贝叶斯方法对参数分布更新。最后,得到个体发动机剩余寿命的实时预测值。通过实例验证本文方法预测的准确性。

     

  • 图 1  剩余寿命的预测流程

    Figure 1.  Prediction process of residual life

    图 2  航空发动机退化路径

    Figure 2.  Degradation path of aeroengines

    图 3  第1阶段剩余寿命概率密度分布

    Figure 3.  Probability density distribution of residual life at the first stage

    图 4  第2阶段剩余寿命概率密度分布

    Figure 4.  Probability density distribution of residual life at the second stage

    表  1  某型号发动机EGTM监测数据

    Table  1.   EGTM monitoring data of a certain model of engine

    当前时间/
    cycle
    发动机编号
    1 2 3 4 5 6 7
    100 69 70.8 74 67 67 61 69
    200 63.3 56.2 70 65 61.4 65.3 63
    3 600 21.6 19 8 -0.3 15 13.9 19.4
    3 700 18.7 17.1 5.6 10.4 12.5 16.5
    3 800 9.5 16.4 -0.1 12.8 6.8 13.6
    3 900 1.2 10.4 -0.9 10.7
    4 000 -0.8 6.8
    下载: 导出CSV

    表  2  超参数的先验估值

    Table  2.   Prior estimates of hyper parameter

    阶段 a b c d r
    1 1.875 8 0.045 7 0.561 3 -1.000 7 0.443 8
    2 0.971 1 0.019 6 0.069 8 -0.166 5 0.791 9
    下载: 导出CSV

    表  3  贝叶斯更新后的参数第1阶段估计结果

    Table  3.   Bayes updated parameter estimation results at the first stage

    当前时间/
    cycle
    超参数 估计
    a b c d u σ2
    1 000 6.875 8 0.830 3 0.382 5 -1.126 2 -1.126 2 0.120 8
    1 500 9.375 8 6.996 6 0.377 3 -1.212 3 -1.212 3 0.756 2
    2 000 11.876 7.208 6 0.377 4 -1.224 4 -1.224 4 0.607 0
    下载: 导出CSV

    表  4  贝叶斯更新后的参数第2阶段估计结果

    Table  4.   Bayes updated parameter estimation results at the second stage

    当前时间/
    cycle
    超参数 估计
    a b c d u σ2
    2 500 3.471 1 0.452 3 0.022 1 -0.080 3 -0.080 3 0.130 3
    3 000 5.971 1 1.988 4 0.015 3 -0.083 4 -0.083 4 0.333 0
    3 500 8.471 1 3.298 6 0.012 2 -0.095 8 -0.095 8 0.389 3
    下载: 导出CSV

    表  5  2种模型相对误差对比结果

    Table  5.   Relative error comparison results of two models

    当前时间/
    cycle
    相对误差
    单阶段线性 多阶段非线性
    1 000 0.046 6 0.018 6
    1 500 0.072 1 0.037 9
    2 000 0.076 8 0.264 2
    2 500 0.182 1 0.175
    3 000 0.624 4 0.49
    3 500 1.235 0 0.518
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-06
  • 录用日期:  2017-08-31
  • 刊出日期:  2018-05-20

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